Intelligent Network Orchestration System for Efficient Deployment and Management of Distributed Deep Neural Networks in Dynamic Network Environments
DOI:
https://doi.org/10.23055/ijietap.2025.32.2.10605Keywords:
Distributed Deep Neural Networks, Intelligent Network Orchestration System, Dynamic Resource Allocation, Deep Reinforcement LearningAbstract
The evolution of distributed deep neural networks (DNNs) in device-edge-cloud architectures necessitates adaptive network control mechanisms to meet stringent latency requirements. Existing software-defined networking (SDN) solutions exhibit limitations in dynamic resource orchestration and cross-layer optimization for AI workloads. We present INOS (Intelligent Network Orchestration System), an SDN-based framework integrating network function virtualization to enable QoS-aware network slicing for prioritized DNN traffic and deep reinforcement learning-driven task offloading across heterogeneous compute nodes. Through NS-3 simulations replicating industrial IoT scenarios, INOS demonstrates quantifiable improvements in latency reduction and resource efficiency compared to static resource allocation baselines. The system architecture extends SDN control plane capabilities with AI-native decision modules, addressing key challenges in service function chaining for distributed intelligence.
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